River water-quality analysis: “critical contaminate detection”, “classification of multiple-water-quality-parameters values” and “real-time notification” by rspa processes

2015 ◽  
Author(s):  
Chalisa Veesommai ◽  
Yasushi Kiyoki
Keyword(s):  
Water Quality ◽  
Real Time ◽  
River Water ◽  
Quality Parameters ◽  
River Water Quality ◽  
Water Quality Parameters ◽  
Quality Analysis ◽  
Water Quality Analysis

scholarly journals Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

2019 ◽  
Vol 12 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Armin Azad ◽  
Hojat Karami ◽  
Saeed Farzin ◽  
Sayed-Farhad Mousavi ◽  
Ozgur Kisi
Keyword(s):  
Water Quality ◽  
River Water ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Parameters ◽  
River Water Quality ◽  
Water Quality Parameters ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Prasad M. Pujar ◽  
Harish H. Kenchannavar ◽  
Raviraj M. Kulkarni ◽  
Umakant P. Kulkarni
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Real Time ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Quality Analysis ◽  
Water Quality Analysis

AbstractIn this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

Effects of Two Types of Landfills on River Water Quality and Endocrine Disruptor Concentrations Before and After Rainfall in a Subtropical Climate

2009 ◽  
Vol 44 (4) ◽  
pp. 355-363 ◽  
Author(s):  
Chia-Wei Lin ◽  
Mei-Hui Li
Keyword(s):  
Water Quality ◽  
River Water ◽  
Quality Parameters ◽  
River Water Quality ◽  
Water Quality Parameters ◽  
Sanitary Landfill ◽  
Garbage Dump ◽  
Before And After ◽  
Dilution Effects

Abstract One closed uncontrolled landfill, the Neihu garbage dump, and one active controlled landfill, the Sanzhuku sanitary landfill, were selected for investigation of their leachate characteristics and effects on adjacent river water quality before and after rainfall in northern Taiwan. A total of seven samplings were made during February and June 2007, with four samplings done after individual rainfall events on study sites. Water quality of runoff samples collected from the Sanzhuku sanitary landfill showed less pollution than the water quality of leachates collected from the Neihu garbage dump; however, some water quality levels of leachate samples collected from the Neihu garbage dump were relatively high, such as ammonia nitrogen (NH3-N), orthophosphate (PO43-) and biochemical oxygen demand (BOD5). At the uncontrolled dump, rainfall lead to dilution effects on river water NH3-N and PO43- concentrations, but not other water quality parameters. In contrast, the concentrations of bisphenol A (BPA) and nonylphenol were increased in both types of landfills after rainfall in the present study. Dilution effects of rainfall on most water quality parameters and toxicity tests were observed in the Neihu garbage dump leachates after rainfall, but not for the Sanzhuku Landfill runoff. The highest concentration of BPA measured in this study was 25.8 μg L-1 in the Sanzhuku sanitary landfill runoff after the heaviest rainfall event, during which 236 mm of rainfall accumulated over four days. The results of this study suggest that both uncontrolled and controlled landfill leachates can be an important potential pollution source of BPA to adjacent water bodies.

scholarly journals Reconstruction of river water quality missing data using artificial neural networks

2015 ◽  
Vol 50 (4) ◽  
pp. 326-335 ◽  
Author(s):  
Hossein Tabari ◽  
P. Hosseinzadeh Talaee
Keyword(s):  
Water Quality ◽  
Missing Data ◽  
River Water ◽  
Missing Values ◽  
Human Life ◽  
Quality Parameters ◽  
River Water Quality ◽  
Water Quality Parameters ◽  
Rbf Networks ◽  
Input Variables

The monitoring of river water quality is important for human life and the health of the environment. However, water quality studies in many parts of the world, especially in developing countries, are restricted by the existence of missing data. In this study, the efficiency of the multilayer perceptron (MLP) and radial basis function (RBF) networks for recovering the missing values of 13 water quality parameters was examined based on data from five stations located along the Maroon River, Iran. The monthly values of other existing water quality parameters were used as input variables to the MLP and RBF models. According to the achieved results, the hardness missing values were estimated precisely by both the MLP and RBF networks, while the worst performance of the networks was found for the turbidity parameter. It was also found that the MLP models were superior to the RBF models to reconstruct water quality missing data.

scholarly journals Wavelet and statistical analyses of river water quality parameters: a case study in the Lower Minnesota River

2018 ◽  
Vol 13 (4) ◽  
pp. 922-931 ◽  
Author(s):  
Ang Gao ◽  
Shiqiang Wu ◽  
Senlin Zhu ◽  
Zhun Xu
Keyword(s):  
Water Quality ◽  
River Water ◽  
Wavelet Decomposition ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
River Water Quality ◽  
Water Quality Parameters ◽  
Strong Negative Correlation ◽  
Minnesota River ◽  
One Year

Abstract Statistical and wavelet analyses are useful tools for analyzing river water quality parameters. In this study, they were employed to study parameters including biochemical oxygen demand (BOD), dissolved oxygen (DO), nitrate (NO3), ammonium (NH4), phosphate (PO4), total phosphorus (TP), total Kjeldahl nitrogen (TKN), chlorophyll a (CHLA), total suspended solids (TSS) and water temperature (TEMP) monitored at five hydrologic stations on the Lower Minnesota River, USA. Strong positive correlations were observed between CHLA-BOD, TP-TKN, TP-TSS and TKN-TSS, with strong negative correlation between DO-TEMP. Daubechies wavelet at level 5 has been calculated for some key water quality parameters as it gives the finer scale approximation and decomposition of each water parameter. The results show that TEMP and DO have relative quasi-periodicity of about one year, while the quasi-periodicity of NH4 and PO4 are weaker than for TEMP and DO. Correlations between some parameters based on wavelet decomposition results are consistent. The fluctuation range characteristics of some parameters were also analyzed through wavelet decomposition.

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